25 seconds with the camera and mic on. While we read your cardiac rhythm, skin, and voice signal, you tell us what you want to improve. The AI weighs everything and writes you a real analysis — not a quiz score.
{status === "idle" && (
When the scan starts, say out loud:
"What I want to improve in 90 days is …" — and just talk. Mention any of:
ⓘ WHAT WE CAPTURE · Heart rhythm via face-color blood flow (rPPG). Skin via CV. Voice via DSP + browser speech-to-text (your goals → AI input). Raw numbers stay private — the AI gives you the analysis.
);
};
const Pill = ({ on, label }) => (
{label}
);
// ── Voice DSP (client-side feature extraction) ──
function computeVoiceFeatures(samplesArrays, sr) {
const total = samplesArrays.reduce((s,c)=>s+c.length, 0);
const samples = new Float32Array(total);
let off = 0;
for (const c of samplesArrays) { samples.set(c, off); off += c.length; }
if (samples.length < sr * 5) return null;
const frameSize = Math.round(sr * 0.05);
const hop = Math.round(sr * 0.025);
const detectPitch = (frame) => {
const minHz = 70, maxHz = 400;
const minLag = Math.floor(sr / maxHz), maxLag = Math.floor(sr / minHz);
let bestLag = 0, bestCorr = 0, sumE = 0;
for (let i = 0; i < frame.length; i++) sumE += frame[i] * frame[i];
if (sumE < 0.001) return 0;
for (let lag = minLag; lag <= maxLag; lag++) {
let corr = 0;
for (let i = 0; i < frame.length - lag; i++) corr += frame[i] * frame[i+lag];
if (corr > bestCorr) { bestCorr = corr; bestLag = lag; }
}
if (bestCorr / sumE < 0.3) return 0;
return sr / bestLag;
};
const rms = (frame) => { let s=0; for (let i=0;i {
const N = 256;
const sub = new Float32Array(N);
const stride = Math.max(1, Math.floor(frame.length / N));
for (let i = 0; i < N; i++) sub[i] = frame[i*stride] || 0;
let num = 0, den = 0;
for (let k = 1; k < N/2; k++) {
let re = 0, im = 0;
for (let n = 0; n < N; n++) {
const a = -2 * Math.PI * k * n / N;
re += sub[n] * Math.cos(a); im += sub[n] * Math.sin(a);
}
const mag = Math.sqrt(re*re + im*im);
const f = (k * sr / stride) / N;
num += f * mag; den += mag;
}
return den > 0 ? num / den : 0;
};
const pitches = [], energies = [], centroids = [];
let voicedFrames = 0, totalFrames = 0;
for (let i = 0; i + frameSize < samples.length; i += hop) {
const frame = samples.subarray(i, i + frameSize);
const e = rms(frame);
energies.push(e); totalFrames++;
if (e < 0.005) continue;
const p = detectPitch(frame);
if (p > 0) {
pitches.push(p); voicedFrames++;
if (totalFrames % 4 === 0) centroids.push(spectralCentroid(frame));
}
}
if (pitches.length < 20) return null;
const mean = (a) => a.reduce((s,x)=>s+x,0)/a.length;
const std = (a) => { const m = mean(a); return Math.sqrt(mean(a.map(x=>(x-m)**2))); };
const meanPitch = mean(pitches);
const stdPitch = std(pitches);
const meanEnergy = mean(energies);
const meanCentroid = centroids.length ? mean(centroids) : 0;
let jitter = 0;
for (let i = 1; i < pitches.length; i++) jitter += Math.abs(pitches[i] - pitches[i-1]);
jitter = (jitter / (pitches.length - 1)) / meanPitch;
const stress_score = Math.round(Math.min(100, Math.max(0, jitter*1500 + (stdPitch < 8 ? 25 : 0) + (meanCentroid < 800 ? 15 : 0))));
const fatigue_score = Math.round(Math.min(100, Math.max(0, (meanEnergy < 0.02 ? 40 : 20) + (meanCentroid < 700 ? 25 : 0) + (stdPitch < 10 ? 20 : 0))));
const energy_score = Math.round(Math.min(100, Math.max(0, meanEnergy*2000 + stdPitch*0.8 + (meanCentroid > 1000 ? 15 : 0))));
const mood_class = stdPitch > 15 && jitter < 0.04 ? "upbeat" : jitter > 0.06 ? "tense" : meanEnergy < 0.02 ? "flat" : "neutral";
return {
ok: true,
voiced_ratio: Math.round((voicedFrames / totalFrames) * 100) / 100,
mean_pitch_hz: Math.round(meanPitch),
pitch_std_hz: Math.round(stdPitch * 10) / 10,
mean_energy: Math.round(meanEnergy * 1000) / 1000,
spectral_centroid_hz: Math.round(meanCentroid),
jitter: Math.round(jitter * 1000) / 1000,
stress_score, fatigue_score, energy_score, mood_class,
};
}
window.BR_STEPS = window.BR_STEPS || {};
window.BR_STEPS.StepBioscan = StepBioscan;